CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li.
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Transcript of CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li.
![Page 1: CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li.](https://reader036.fdocuments.net/reader036/viewer/2022062315/56649e4b5503460f94b4020c/html5/thumbnails/1.jpg)
CS55 Tianfan Xue 2005011371Adviser: Bo Zhang, Jianmin Li
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OutlineIntroductionOriginal AlgorithmImproved AlgorithmSystem Design & Data SetPerformance EvaluationWork Next Step
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IntroductionAutomatically Video SurveillanceHuman Tracking
What is human trackingWhy do human tracking
PresumptionPerson is standing & Normal
Pose
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Original AlgorithmAlgorithm Design
General FrameworkProbability EvaluationHOG featureInitial DetectMotion Prediction
Drawback
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Original AlgorithmGeneral Framework
Frame n
State n-1Predicted State n
HumanDetector(HOG)
State n
Motion prediction & Gauss Diffusion
Position & Size
HOG features validation
Training Set Machine learning
Offline
Online
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Original AlgorithmProbability Evaluation
Definitionxt : State in time t
zt : Image in time t Zt : Whole image sequence till time t
Probability:1 1 1 1( | ) ( | ) ( | ) ( | )t t t t t t t t tp x Z p z x p x x p x Z dx
1( | ) ( | ) ( | )i i i i it t t t t tx Z p x x p z x
Gauss Model + Motion Predict
HOG output
Simplified in Particle Filter
1
Ni i
t ti
x x
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Original AlgorithmInitial Detect
Randomly Choose 2000 positions in an imageMotion Prediction
Linear Regression of recent 10 frameOffline Detector
HOG features
original Edge map
HOG
SVM
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Original AlgorithmDrawbacks
Fail to find a person at emergence Detection Rate ↔
Computational ComplexityLoss track when partially Occlusion2-Magnet Effect
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Original AlgorithmDrawbacks
Fail to find a person at emergence
Loss track when partially Occlusion
2-Magnet Effect
![Page 10: CS55 Tianfan Xue 2005011371 Adviser: Bo Zhang, Jianmin Li.](https://reader036.fdocuments.net/reader036/viewer/2022062315/56649e4b5503460f94b4020c/html5/thumbnails/10.jpg)
Original AlgorithmDrawbacks
Fail to find a person at emergence
Loss track when partially Occlusion
2-Magnet EffectWhen person A (more obvious) pass person B(less obvious), A will attract B’s window
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Improved Algorithm3 Improvement
Use salience to cut search spaceCombine offline-online classifier(online: Color features)Part Detector
Problems
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Improved AlgorithmUsing Salience To Cut
Search SpaceIdea:
The position people more like emerge (Salience)
Method:Detect at only at position with great variance
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Improved AlgorithmCombine offline-online classifier(online: Color features)
Frame n
State n-1Color detect result
Predicted State n
HOGClassifier
Final result
Motion prediction & Gauss Diffusion
Size & position
Color features validation
HOG features validation
ColorClassifier
Training Set
Machine learning
Offline
Online
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Improved SystemPart Detector (CVPR05’s, Bo Wu)
7%
32%
49%
93%
20%
64%
10%
24%
46%
82%
21%
77%
12.5% 87.5%
34% 65%
31% 68%
HS
Torso
Leg
HS
Torso
Leg
Color Part
Whole
27% 63%
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Improved SystemPart Detector 2
LegColor Model
Not Visible
TorsoColor Model
Visible
HSColor Model
Visible
TorsoHOG
Model
HSHOG
ModelFinal Property
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Improved SystemProblems
Color model also learns the occlusion object→ Always Output that all parts is visible
When a person disappear, the corresponding detect window still exists
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System DesignTracking SystemXML Debugging outputGUI
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Data SetTraining Data
INRIA Person Data Set2416 Positive Examples, 1218 Negative Examples
Testing DataPETS2004(CAVIAR)
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Experiment ResultEvaluation
Compare ground truth windows with detected windowsOverlap:(T=0.5)
Tracker Detection Rate(TRDR) & False Alarm Rate(FAR)
| |2*| | | |
obs truth
obs truth
A AT
A A
TPTRDR
TP FN
FP
FARTP FP
TP: True Positive, FP: False Positive, FN: False Negative
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Experiment ResultBaseline: With Color Model, With Salience DetectTest1 Use Salience to Detect New Person
Random Select Detect
Pos
Select At Salience
Time 15.9s/frame 4.5s/frame
TRDR 61.1% 66.8%
FAR 21.9% 15.6%Test2 Color ModelWithout Color
ModelWith Color
Model
Time 2.2s/frame 4.5s/frame
TRDR 9.8% 66.8%
FAR 20.4% 15.6%
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Work Next StepImprove online-offline classifier
How to learn a good color modelHow to decide a person is disappeared
Make a more wide-arrange evaluation
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Q & A
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Probability EvaluationBayesian result
Particle Filter1 1 1 1( | ) ( | ) ( | ) ( | )t t t t t t t t tp x Z p z x p x x p x Z dx
1( | ) ( | ) ( | )t t t t t tx Z p z x p x x Space Too Large!!!
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2-Magnet EffectSolve 2-Magnet Effect
But it will bring some new problems…
1( | ) ( | ) ( | ) ( )t i t t t t overlapx Z p x x p z x p x
Gauss Model + Motion Predict
HOG output Punishment for 2 close windows
No ColorNo overlap
term
No ColorOverlap term
ColorNo overlap
term
Coloroverlap term
TRDR 46.9% 9.8% 66.8% 9.8%
FAR 42.1% 20.4% 15.6% 20.0%
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Color ModelFeatures:
72-dim HSV histogramProbability Evaluation:
Inner Product of 2 feature vectors
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Detect ResultPerformance of other algorithm (Here, different
evaluation standard was used)TRDR FAR
Our Method 56.1% 29.4%
BBS 42.5% 72.4%
W4 11.7% 92.1%
SGM 42.8% 54.0%
MGM 38.2% 63.3%
LOTS 47.9% 40.3%
Track 44.4% 35.2%